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EIS Toolkit implementation status

Status symbols:

• 🟩 = Completed
• 🟦 = In process / in review
• 🟥 = Not yet started

For a more detailed set of included tools / functions and their documentation, check the documentation site.

Raster processing

Tool	Status
Reproject raster	🟩
Resample raster	🟩
Clip raster	🟩
Snap raster	🟩
Window raster	🟩
Unify rasters	🟩
Surface derivative tools	🟩
Extract values from raster	🟩
Reclassify	🟩
Create constant raster	🟩
Unique conditions grid	🟩
Smoothing/filter tools	🟩
Distance to anomaly	🟩

Vector processing

Tool	Status
Reproject vector	🟩
Interpolation - IDW	🟩
Interpolation - kriging	🟩
Density computation	🟩
Distance computation	🟩
Calculate geometry	🟩
Extract shared lines	🟩
Cell-based association	🟩
Rasterize	🟩
Proximity computation	🟦

Exploratory analyses

Tool	Status
PCA	🟩
K-means clustering	🟩
DBSCAN	🟩
Descriptive statistics	🟩
Statistical hypothesis tests	🟩
Basic plots (scatter, line, bar, hist, kde, ecdf, heatmap, pairplot, regrplot)	🟩
Parallel coordinates plot	🟩
Feature importance	🟩
Local Moran's I	🟩

Data transformations

Tool	Status
Binarize	🟩
Winsorize	🟩
Logarithmic	🟩
Min-max	🟩
Z-score normalize	🟩
Clip transform	🟩
One-hot encode	🟩
CoDa transformations	🟩

Training data tools

Tool	Status
Balance data (SMOTETomek)	🟩
Split data	🟩
Data sampler	🟦

Prediction

Tool	Status
Fuzzy overlay	🟩
Weights of evidence	🟩
Logistic regression	🟩
Random forest (regr+cls)	🟩
Gradient boosting (regr+csl)	🟩
MLP (multilayer perceptron, regr+cls)	🟩
BNN	🟦

Evaluation

Tool	Status
Calculate base metrics	🟩
Summarize label metrics for binary classifier	🟩
Summarize probability metrics for binary classifier	🟩
Plot ROC curve	🟩
Plot DET curve	🟩
Plot precision-recall curve	🟩
Plot calibration curve	🟩
Plot distribution of predicted probabilities	🟩
Plot confusion matrix	🟩
Plot neural network training accuracy	🟩
Plot neural network training loss	🟩
Plot prediction area curves	🟩
Plot rate curve	🟩
Score model (accuracy, precision, recall, F1, MAE, MSE, RMSE, R2)	🟩